Overview

Dataset statistics

Number of variables17
Number of observations8971
Missing cells21805
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory124.0 B

Variable types

NUM15
CAT2

Warnings

day365 is highly correlated with day180 and 5 other fieldsHigh correlation
day180 is highly correlated with day365 and 5 other fieldsHigh correlation
day545 is highly correlated with day180 and 5 other fieldsHigh correlation
day730 is highly correlated with day180 and 5 other fieldsHigh correlation
day1095 is highly correlated with day180 and 5 other fieldsHigh correlation
day1460 is highly correlated with day180 and 5 other fieldsHigh correlation
day1825 is highly correlated with day180 and 5 other fieldsHigh correlation
day180 has 502 (5.6%) missing values Missing
day365 has 1054 (11.7%) missing values Missing
day545 has 1843 (20.5%) missing values Missing
day730 has 2703 (30.1%) missing values Missing
day1095 has 4153 (46.3%) missing values Missing
day1460 has 5141 (57.3%) missing values Missing
day1825 has 6317 (70.4%) missing values Missing
api has unique values Unique
hybrid_collect has 7370 (82.2%) zeros Zeros
slickwater_collect has 3046 (34.0%) zeros Zeros
gel_collect has 5405 (60.2%) zeros Zeros

Reproduction

Analysis started2020-11-30 18:44:17.532539
Analysis finished2020-11-30 18:45:03.652310
Duration46.12 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

api
Real number (ℝ≥0)

UNIQUE

Distinct8971
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.223603369e+12
Minimum5.00109742e+12
Maximum4.903120131e+13
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:03.759521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.00109742e+12
5-th percentile5.123330865e+12
Q15.123376475e+12
median5.12340919e+12
Q35.123448245e+12
95-th percentile5.12350136e+12
Maximum4.903120131e+13
Range4.403010389e+13
Interquartile range (IQR)71770000

Descriptive statistics

Standard deviation9.378947041e+12
Coefficient of variation (CV)1.298375141
Kurtosis15.92249159
Mean7.223603369e+12
Median Absolute Deviation (MAD)35460000
Skewness4.233064178
Sum6.480294582e+16
Variance8.796464759e+25
MonotocityNot monotonic
2020-11-30T18:45:03.967981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.12343481e+121< 0.1%
 
5.12339353e+121< 0.1%
 
5.12345533e+121< 0.1%
 
5.1234095e+121< 0.1%
 
5.12348042e+121< 0.1%
 
5.12343002e+121< 0.1%
 
5.12336899e+121< 0.1%
 
5.12333424e+121< 0.1%
 
5.12337063e+121< 0.1%
 
5.12339236e+121< 0.1%
 
Other values (8961)896199.9%
 
ValueCountFrequency (%) 
5.00109742e+121< 0.1%
 
5.00109753e+121< 0.1%
 
5.00109754e+121< 0.1%
 
5.0010976e+121< 0.1%
 
5.00109772e+121< 0.1%
 
ValueCountFrequency (%) 
4.903120131e+131< 0.1%
 
4.903120064e+131< 0.1%
 
4.902127986e+131< 0.1%
 
4.902127869e+131< 0.1%
 
4.902127814e+131< 0.1%
 

State
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size70.1 KiB
COLORADO
8539 
WYOMING
 
430
ValueCountFrequency (%) 
COLORADO853995.2%
 
WYOMING4304.8%
 
(Missing)2< 0.1%
 
2020-11-30T18:45:04.190261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:45:04.468575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:04.584719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.950953071
Min length3

TotalCleanVol
Real number (ℝ≥0)

Distinct8723
Distinct (%)97.7%
Missing43
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean164681.1792
Minimum2814
Maximum1330817
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:04.756330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2814
5-th percentile50219.15
Q177918.5
median133340
Q3202932.5
95-th percentile416048.1
Maximum1330817
Range1328003
Interquartile range (IQR)125014

Descriptive statistics

Standard deviation121781.9819
Coefficient of variation (CV)0.7395015175
Kurtosis7.430473158
Mean164681.1792
Median Absolute Deviation (MAD)59481.5
Skewness2.170306418
Sum1470273568
Variance1.483085112e+10
MonotocityNot monotonic
2020-11-30T18:45:04.973871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
59407320.4%
 
1253763< 0.1%
 
688943< 0.1%
 
954803< 0.1%
 
1718882< 0.1%
 
1214102< 0.1%
 
1511302< 0.1%
 
367622< 0.1%
 
639582< 0.1%
 
1391362< 0.1%
 
Other values (8713)887598.9%
 
(Missing)430.5%
 
ValueCountFrequency (%) 
28141< 0.1%
 
28261< 0.1%
 
41251< 0.1%
 
65061< 0.1%
 
65451< 0.1%
 
ValueCountFrequency (%) 
13308171< 0.1%
 
12718661< 0.1%
 
11189231< 0.1%
 
10793611< 0.1%
 
10572761< 0.1%
 

hybrid_collect
Real number (ℝ≥0)

ZEROS

Distinct1585
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20177.83135
Minimum0
Maximum879481
Zeros7370
Zeros (%)82.2%
Memory size35.0 KiB
2020-11-30T18:45:05.191942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile139783
Maximum879481
Range879481
Interquartile range (IQR)0

Descriptive statistics

Standard deviation58276.72611
Coefficient of variation (CV)2.888156072
Kurtosis36.76131337
Mean20177.83135
Median Absolute Deviation (MAD)0
Skewness4.817451125
Sum181015325
Variance3396176807
MonotocityNot monotonic
2020-11-30T18:45:05.392261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0737082.2%
 
688943< 0.1%
 
283622< 0.1%
 
1124042< 0.1%
 
822272< 0.1%
 
2551922< 0.1%
 
1089082< 0.1%
 
812852< 0.1%
 
661712< 0.1%
 
439232< 0.1%
 
Other values (1575)158217.6%
 
ValueCountFrequency (%) 
0737082.2%
 
891< 0.1%
 
1901< 0.1%
 
2171< 0.1%
 
2591< 0.1%
 
ValueCountFrequency (%) 
8794811< 0.1%
 
8719821< 0.1%
 
7405021< 0.1%
 
7383131< 0.1%
 
7145871< 0.1%
 

slickwater_collect
Real number (ℝ≥0)

ZEROS

Distinct5824
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98162.91941
Minimum0
Maximum1329061
Zeros3046
Zeros (%)34.0%
Memory size35.0 KiB
2020-11-30T18:45:05.606053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52145
Q3146825.5
95-th percentile379120
Maximum1329061
Range1329061
Interquartile range (IQR)146825.5

Descriptive statistics

Standard deviation131860.8044
Coefficient of variation (CV)1.343285277
Kurtosis6.296602213
Mean98162.91941
Median Absolute Deviation (MAD)52145
Skewness2.124408238
Sum880619550
Variance1.738727173e+10
MonotocityNot monotonic
2020-11-30T18:45:05.834195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0304634.0%
 
11127310.3%
 
460263< 0.1%
 
106663< 0.1%
 
721543< 0.1%
 
1263582< 0.1%
 
2421162< 0.1%
 
833252< 0.1%
 
380962< 0.1%
 
1085422< 0.1%
 
Other values (5814)587565.5%
 
ValueCountFrequency (%) 
0304634.0%
 
711< 0.1%
 
771< 0.1%
 
972< 0.1%
 
1011< 0.1%
 
ValueCountFrequency (%) 
13290611< 0.1%
 
12701161< 0.1%
 
11174121< 0.1%
 
9772251< 0.1%
 
9754101< 0.1%
 

gel_collect
Real number (ℝ≥0)

ZEROS

Distinct3468
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33540.37588
Minimum0
Maximum645479
Zeros5405
Zeros (%)60.2%
Memory size35.0 KiB
2020-11-30T18:45:06.052858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351801
95-th percentile169909.5
Maximum645479
Range645479
Interquartile range (IQR)51801

Descriptive statistics

Standard deviation64164.47765
Coefficient of variation (CV)1.913051836
Kurtosis12.3814224
Mean33540.37588
Median Absolute Deviation (MAD)0
Skewness3.011582018
Sum300890712
Variance4117080192
MonotocityNot monotonic
2020-11-30T18:45:06.247227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0540560.2%
 
48280290.3%
 
144923< 0.1%
 
1080792< 0.1%
 
28112< 0.1%
 
645212< 0.1%
 
744252< 0.1%
 
671362< 0.1%
 
723342< 0.1%
 
527912< 0.1%
 
Other values (3458)352039.2%
 
ValueCountFrequency (%) 
0540560.2%
 
241< 0.1%
 
301< 0.1%
 
351< 0.1%
 
401< 0.1%
 
ValueCountFrequency (%) 
6454791< 0.1%
 
6359551< 0.1%
 
6029491< 0.1%
 
5580411< 0.1%
 
5568101< 0.1%
 

Latitude
Real number (ℝ≥0)

Distinct8011
Distinct (%)89.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.37661924
Minimum39.6080407
Maximum42.104219
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:06.454588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum39.6080407
5-th percentile40.01669581
Q140.1469952
median40.35947564
Q340.49559932
95-th percentile40.9739586
Maximum42.104219
Range2.4961783
Interquartile range (IQR)0.34860412

Descriptive statistics

Standard deviation0.3022016851
Coefficient of variation (CV)0.007484571289
Kurtosis1.989136556
Mean40.37661924
Median Absolute Deviation (MAD)0.17695407
Skewness1.077587371
Sum362137.898
Variance0.0913258585
MonotocityNot monotonic
2020-11-30T18:45:06.671946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40.4646113120.1%
 
39.998672120.1%
 
40.20281110.1%
 
40.17203100.1%
 
40.1320595990.1%
 
40.4388390.1%
 
40.3701780.1%
 
40.0735474380.1%
 
40.01220680.1%
 
40.25963980.1%
 
Other values (8001)887498.9%
 
ValueCountFrequency (%) 
39.60804071< 0.1%
 
39.61291< 0.1%
 
39.6130111< 0.1%
 
39.61421471< 0.1%
 
39.614544691< 0.1%
 
ValueCountFrequency (%) 
42.1042191< 0.1%
 
42.0594051< 0.1%
 
42.0458531< 0.1%
 
42.0321081< 0.1%
 
42.0031121< 0.1%
 

Longitude
Real number (ℝ)

Distinct8187
Distinct (%)91.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-104.6121814
Minimum-105.9919448
Maximum-103.7248046
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:06.886020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-105.9919448
5-th percentile-104.9969189
Q1-104.8410646
median-104.671662
Q3-104.44798
95-th percentile-103.8842004
Maximum-103.7248046
Range2.2671402
Interquartile range (IQR)0.3930846

Descriptive statistics

Standard deviation0.3062217423
Coefficient of variation (CV)-0.002927209223
Kurtosis0.55704051
Mean-104.6121814
Median Absolute Deviation (MAD)0.189027
Skewness0.9753312732
Sum-938266.6552
Variance0.09377175546
MonotocityNot monotonic
2020-11-30T18:45:07.092419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-104.7667825120.1%
 
-104.957139120.1%
 
-104.926457120.1%
 
-104.58137110.1%
 
-104.6668110.1%
 
-104.53751100.1%
 
-104.7738264100.1%
 
-104.60033100.1%
 
-104.933139100.1%
 
-104.629102290.1%
 
Other values (8177)886298.8%
 
ValueCountFrequency (%) 
-105.99194481< 0.1%
 
-105.93590241< 0.1%
 
-105.0541171< 0.1%
 
-105.05385341< 0.1%
 
-105.05375281< 0.1%
 
ValueCountFrequency (%) 
-103.72480461< 0.1%
 
-103.7344641< 0.1%
 
-103.742551< 0.1%
 
-103.75390641< 0.1%
 
-103.76140021< 0.1%
 

formation
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
NIOBRARA
6835 
CODELL
2124 
GREENHORN
 
10
SUSSEX
 
2
ValueCountFrequency (%) 
NIOBRARA683576.2%
 
CODELL212423.7%
 
GREENHORN100.1%
 
SUSSEX2< 0.1%
 
2020-11-30T18:45:07.439998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:45:07.568044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:07.717445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length7.527143016
Min length6

day180
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8036
Distinct (%)94.9%
Missing502
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean47451.94568
Minimum408
Maximum216956
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:07.904864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum408
5-th percentile15730.4
Q129472
median42702
Q361138
95-th percentile94037
Maximum216956
Range216548
Interquartile range (IQR)31666

Descriptive statistics

Standard deviation24837.41903
Coefficient of variation (CV)0.5234225629
Kurtosis1.76366997
Mean47451.94568
Median Absolute Deviation (MAD)15083
Skewness1.067613116
Sum401870528
Variance616897383.9
MonotocityNot monotonic
2020-11-30T18:45:08.102369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
486904< 0.1%
 
386854< 0.1%
 
389983< 0.1%
 
457673< 0.1%
 
488213< 0.1%
 
322623< 0.1%
 
382053< 0.1%
 
307353< 0.1%
 
503563< 0.1%
 
442543< 0.1%
 
Other values (8026)843794.0%
 
(Missing)5025.6%
 
ValueCountFrequency (%) 
4081< 0.1%
 
4491< 0.1%
 
4531< 0.1%
 
5671< 0.1%
 
6061< 0.1%
 
ValueCountFrequency (%) 
2169561< 0.1%
 
1991841< 0.1%
 
1701371< 0.1%
 
1686771< 0.1%
 
1682111< 0.1%
 

day365
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7660
Distinct (%)96.8%
Missing1054
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean70122.33649
Minimum215
Maximum414735
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:08.306929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum215
5-th percentile22642.6
Q141944
median61445
Q390494
95-th percentile144093.6
Maximum414735
Range414520
Interquartile range (IQR)48550

Descriptive statistics

Standard deviation39442.4881
Coefficient of variation (CV)0.5624810876
Kurtosis3.287234184
Mean70122.33649
Median Absolute Deviation (MAD)22460
Skewness1.350948135
Sum555158538
Variance1555709867
MonotocityNot monotonic
2020-11-30T18:45:08.495956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
531793< 0.1%
 
573063< 0.1%
 
475943< 0.1%
 
477803< 0.1%
 
850223< 0.1%
 
703813< 0.1%
 
245712< 0.1%
 
486222< 0.1%
 
383252< 0.1%
 
186082< 0.1%
 
Other values (7650)789188.0%
 
(Missing)105411.7%
 
ValueCountFrequency (%) 
2151< 0.1%
 
8581< 0.1%
 
16971< 0.1%
 
18561< 0.1%
 
19031< 0.1%
 
ValueCountFrequency (%) 
4147351< 0.1%
 
3549751< 0.1%
 
2991941< 0.1%
 
2930851< 0.1%
 
2920431< 0.1%
 

day545
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6934
Distinct (%)97.3%
Missing1843
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean81492.62767
Minimum215
Maximum509699
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:08.687198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum215
5-th percentile26688.15
Q148398.25
median70089
Q3104418
95-th percentile171842.7
Maximum509699
Range509484
Interquartile range (IQR)56019.75

Descriptive statistics

Standard deviation47229.21373
Coefficient of variation (CV)0.5795519802
Kurtosis4.375914711
Mean81492.62767
Median Absolute Deviation (MAD)25589
Skewness1.533765439
Sum580879450
Variance2230598630
MonotocityNot monotonic
2020-11-30T18:45:08.887001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
518853< 0.1%
 
460993< 0.1%
 
354773< 0.1%
 
554793< 0.1%
 
878593< 0.1%
 
1326972< 0.1%
 
775992< 0.1%
 
334292< 0.1%
 
698322< 0.1%
 
891342< 0.1%
 
Other values (6924)710379.2%
 
(Missing)184320.5%
 
ValueCountFrequency (%) 
2151< 0.1%
 
21261< 0.1%
 
25441< 0.1%
 
30031< 0.1%
 
33541< 0.1%
 
ValueCountFrequency (%) 
5096991< 0.1%
 
4602221< 0.1%
 
4151931< 0.1%
 
3649081< 0.1%
 
3642231< 0.1%
 

day730
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6129
Distinct (%)97.8%
Missing2703
Missing (%)30.1%
Infinite0
Infinite (%)0.0%
Mean87848.79627
Minimum215
Maximum526188
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:09.085741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum215
5-th percentile29242.95
Q152687
median74740.5
Q3111189
95-th percentile188100.25
Maximum526188
Range525973
Interquartile range (IQR)58502

Descriptive statistics

Standard deviation51899.0393
Coefficient of variation (CV)0.5907768974
Kurtosis4.770352164
Mean87848.79627
Median Absolute Deviation (MAD)26933
Skewness1.669444134
Sum550636255
Variance2693510280
MonotocityNot monotonic
2020-11-30T18:45:09.296169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
331953< 0.1%
 
527163< 0.1%
 
710803< 0.1%
 
646582< 0.1%
 
584162< 0.1%
 
828672< 0.1%
 
745742< 0.1%
 
598412< 0.1%
 
887402< 0.1%
 
878082< 0.1%
 
Other values (6119)624569.6%
 
(Missing)270330.1%
 
ValueCountFrequency (%) 
2151< 0.1%
 
24861< 0.1%
 
34441< 0.1%
 
35871< 0.1%
 
38751< 0.1%
 
ValueCountFrequency (%) 
5261881< 0.1%
 
4665721< 0.1%
 
4158411< 0.1%
 
4053271< 0.1%
 
4046531< 0.1%
 

day1095
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4735
Distinct (%)98.3%
Missing4153
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean91696.94105
Minimum5045
Maximum434958
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:09.506332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5045
5-th percentile32723.1
Q157481.75
median79843
Q3113526
95-th percentile193168.4
Maximum434958
Range429913
Interquartile range (IQR)56044.25

Descriptive statistics

Standard deviation51004.63675
Coefficient of variation (CV)0.556230515
Kurtosis3.626803989
Mean91696.94105
Median Absolute Deviation (MAD)26325
Skewness1.525378144
Sum441795862
Variance2601472970
MonotocityNot monotonic
2020-11-30T18:45:09.698189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
743392< 0.1%
 
386672< 0.1%
 
452092< 0.1%
 
640942< 0.1%
 
773852< 0.1%
 
1043402< 0.1%
 
622682< 0.1%
 
577942< 0.1%
 
522532< 0.1%
 
840852< 0.1%
 
Other values (4725)479853.5%
 
(Missing)415346.3%
 
ValueCountFrequency (%) 
50451< 0.1%
 
52531< 0.1%
 
66951< 0.1%
 
69721< 0.1%
 
78571< 0.1%
 
ValueCountFrequency (%) 
4349581< 0.1%
 
4180961< 0.1%
 
4148701< 0.1%
 
3965701< 0.1%
 
3714471< 0.1%
 

day1460
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3770
Distinct (%)98.4%
Missing5141
Missing (%)57.3%
Infinite0
Infinite (%)0.0%
Mean93808.23133
Minimum6620
Maximum372235
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:09.896978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6620
5-th percentile36088.75
Q160799
median82680
Q3114853.25
95-th percentile191336.55
Maximum372235
Range365615
Interquartile range (IQR)54054.25

Descriptive statistics

Standard deviation49532.09836
Coefficient of variation (CV)0.5280144147
Kurtosis3.104722613
Mean93808.23133
Median Absolute Deviation (MAD)25466
Skewness1.480056797
Sum359285526
Variance2453428768
MonotocityNot monotonic
2020-11-30T18:45:10.104758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
324592< 0.1%
 
817592< 0.1%
 
946212< 0.1%
 
803732< 0.1%
 
770712< 0.1%
 
1056902< 0.1%
 
542252< 0.1%
 
600232< 0.1%
 
797142< 0.1%
 
713902< 0.1%
 
Other values (3760)381042.5%
 
(Missing)514157.3%
 
ValueCountFrequency (%) 
66201< 0.1%
 
75381< 0.1%
 
82921< 0.1%
 
90151< 0.1%
 
91251< 0.1%
 
ValueCountFrequency (%) 
3722351< 0.1%
 
3706221< 0.1%
 
3433531< 0.1%
 
3411031< 0.1%
 
3386791< 0.1%
 

day1825
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2632
Distinct (%)99.2%
Missing6317
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean96202.01959
Minimum9278
Maximum400751
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:10.478255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9278
5-th percentile38378.95
Q163796.25
median86448
Q3116765.75
95-th percentile189775.75
Maximum400751
Range391473
Interquartile range (IQR)52969.5

Descriptive statistics

Standard deviation48241.85126
Coefficient of variation (CV)0.5014640177
Kurtosis3.363213644
Mean96202.01959
Median Absolute Deviation (MAD)25600.5
Skewness1.452819404
Sum255320160
Variance2327276213
MonotocityNot monotonic
2020-11-30T18:45:10.687498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
717642< 0.1%
 
897002< 0.1%
 
952542< 0.1%
 
444222< 0.1%
 
843172< 0.1%
 
1649062< 0.1%
 
685862< 0.1%
 
699782< 0.1%
 
1052822< 0.1%
 
841882< 0.1%
 
Other values (2622)263429.4%
 
(Missing)631770.4%
 
ValueCountFrequency (%) 
92781< 0.1%
 
96141< 0.1%
 
96541< 0.1%
 
105381< 0.1%
 
117491< 0.1%
 
ValueCountFrequency (%) 
4007511< 0.1%
 
3511821< 0.1%
 
3470151< 0.1%
 
3413541< 0.1%
 
3331941< 0.1%
 

TotalProppant
Real number (ℝ≥0)

Distinct8723
Distinct (%)97.7%
Missing43
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6863428.374
Minimum10833
Maximum39844907
Zeros0
Zeros (%)0.0%
Memory size70.1 KiB
2020-11-30T18:45:10.912479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10833
5-th percentile1849439.2
Q13641288.5
median5101441.5
Q38842257.5
95-th percentile17497438.15
Maximum39844907
Range39834074
Interquartile range (IQR)5200969

Descriptive statistics

Standard deviation4954765.145
Coefficient of variation (CV)0.7219081886
Kurtosis4.431899216
Mean6863428.374
Median Absolute Deviation (MAD)2207689.5
Skewness1.822383067
Sum6.127668853e+10
Variance2.454969765e+13
MonotocityNot monotonic
2020-11-30T18:45:11.167648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3679086230.3%
 
3750000170.2%
 
3678540110.1%
 
502500050.1%
 
602000050.1%
 
440000050.1%
 
550000050.1%
 
83970004< 0.1%
 
47180004< 0.1%
 
168480004< 0.1%
 
Other values (8713)884598.6%
 
(Missing)430.5%
 
ValueCountFrequency (%) 
108331< 0.1%
 
869771< 0.1%
 
970151< 0.1%
 
1267501< 0.1%
 
1298711< 0.1%
 
ValueCountFrequency (%) 
398449071< 0.1%
 
394697571< 0.1%
 
383765201< 0.1%
 
382750201< 0.1%
 
357109701< 0.1%
 

Interactions

2020-11-30T18:44:21.636248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:21.797628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:21.961004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:22.122476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:22.286928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:22.555354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:22.720797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:22.882390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.041643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.205080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.356877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.513415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.683192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:23.850672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.024727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.195498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.360677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.535748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.708058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:24.883453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.051575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.270633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.448495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.620389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.796602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:25.969791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:26.139007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:26.320144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:26.498292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:26.686015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:26.870028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:27.029576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:27.202518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:27.367026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:27.542122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:27.844210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.012793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.182937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.350428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.522663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.681406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:28.845060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.017918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.188727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.367024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.539864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.704027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:29.888510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.057292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.235266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.397320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.571645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.753102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:30.925377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:31.104881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:31.274415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:31.444258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:31.622512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:31.826532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.010993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.191224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.343701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.504782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.663785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:32.826896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.122908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.287007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.452693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.615020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.780962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:33.933867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.087666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.257278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.420463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.589804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.760125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:34.925470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.102564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.277359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.453584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.622661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.796129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:35.985286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:36.170756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:36.351374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:36.516299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:36.687159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:36.865787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.049901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.232420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.414765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.588752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.771195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:37.948195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:38.129324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:38.439021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:38.620202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:38.794684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:38.973548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:39.153543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:39.320160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:39.490060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:39.669705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:39.853296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.041270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.220973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.383272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.555312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.720084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:40.890237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.054033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.225163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.399182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.570924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.752526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:41.915883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.084317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.261055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.445334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.638487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.822740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:42.992655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:43.174892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:43.355108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:43.672983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:43.850568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.027253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.209998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.393718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.580722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.749574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:44.937805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:45.123704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:45.311238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:45.505768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:45.695081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:45.850293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.010938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.168102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.330976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.483256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.642369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.805725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:46.970905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.134062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.286442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.445405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.613101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.778539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:47.952349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:48.120897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:48.281713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:48.453100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:48.617588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:48.931847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.090270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.260926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.429859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.595105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.766033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:49.922355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.082360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.252449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.421599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.595495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.770446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:50.939958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:51.119139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:51.293507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:51.477406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:51.651708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:51.834132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.013476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.191905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.372394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.543992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.739511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:52.923009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:53.106676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:53.294894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:53.479892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:53.644531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:53.823426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:54.142384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:54.324014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:54.493930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:54.676294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:54.857971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.039827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.219901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.391747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.564557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.746095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:55.937941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:56.130867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:56.319189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:56.492978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:56.681736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:56.862569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.052022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.224461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.413620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.605236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.792017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:57.982725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:58.153811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:58.337592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:58.526836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:58.717311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:58.908906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:59.100756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:59.413871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:59.600045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:59.782953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:44:59.971213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:00.148990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:00.338536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:00.522506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:00.710941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:00.898843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:01.078432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:01.253494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:01.439988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:01.626368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:01.818121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-30T18:45:11.384768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-30T18:45:11.709193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-30T18:45:12.038370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-30T18:45:12.364904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-30T18:45:12.649858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-30T18:45:02.177918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:02.712742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:03.115762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:45:03.457635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

apiStateTotalCleanVolhybrid_collectslickwater_collectgel_collectLatitudeLongitudeformationday180day365day545day730day1095day1460day1825TotalProppant
05123450470000COLORADO97227.0091186040.121508-104.902747NIOBRARA34629.045574.052115.056460.0NaNNaNNaN5720290.0
15123320810100COLORADO17045.002173957440.930917-104.467586NIOBRARA12174.015003.016390.017777.019082.020067.020926.0577200.0
25123403830000COLORADO146513.0064588040.369187-104.524494NIOBRARA44756.065665.079842.089154.0103961.0NaNNaN8452600.0
35123376830000COLORADO102024.00102024040.010014-104.791563CODELL39490.055857.067039.076653.090541.0100144.0107214.03406480.0
45123360060000COLORADO184744.001602882199240.073949-104.710424NIOBRARA60833.088075.0105605.0119686.0135440.0146147.0154308.05624030.0
55123377060000COLORADO65210.0065210040.509264-104.779784NIOBRARA10544.018756.022896.024975.028069.030241.0NaN4195880.0
65123377340000COLORADO65892.00228324306040.509263-104.779874NIOBRARA11366.016721.019514.023535.029542.033470.0NaN4256820.0
75123440840000COLORADO127878.00318669433340.365409-104.629102NIOBRARA57339.081471.0NaNNaNNaNNaNNaN7262000.0
85123453750000COLORADO366038.00365236040.151840-104.534823NIOBRARA87895.0146091.0180770.0NaNNaNNaNNaN10929589.0
95123372760000COLORADO79320.00425703600040.518015-104.720354CODELL32284.045911.054622.060890.069639.076069.082387.03526080.0

Last rows

apiStateTotalCleanVolhybrid_collectslickwater_collectgel_collectLatitudeLongitudeformationday180day365day545day730day1095day1460day1825TotalProppant
89615123477070000COLORADO606572.00587236040.217228-104.587743NIOBRARA127355.0189428.0NaNNaNNaNNaNNaN19189890.0
89625123416450000COLORADO183191.00182881040.853957-103.800187NIOBRARA38429.074895.089690.095603.0NaNNaNNaN4792428.0
89635123426110000COLORADO120302.00117052040.071857-104.777480CODELL69346.0101079.0118978.0130420.0NaNNaNNaN3520900.0
89645123448650000COLORADO306220.00288617040.066461-104.965488NIOBRARA105012.0164847.0189603.0NaNNaNNaNNaN13335390.0
89655123460330000COLORADO600917.00600806040.541986-104.759331NIOBRARA42116.076037.0NaNNaNNaNNaNNaN13186000.0
896649021210600000WYOMING113084.00011308441.101122-104.682676CODELL69240.080651.094600.0108408.0122126.0128154.0133519.010987347.0
896749021211170000WYOMING130089.00600826790641.298136-104.633688CODELL78702.0120979.0145865.0157424.0188097.0215945.0237755.06204780.0
89685001103760000COLORADO147619.00014761939.998672-104.851392NIOBRARANaNNaNNaNNaNNaNNaNNaN6844000.0
89695123462860000COLORADO215601.00021528440.460545-104.766783NIOBRARA58227.087562.0NaNNaNNaNNaNNaN12501070.0
89705123410380000COLORADO148777.000040.714772-104.035384NIOBRARA33952.051954.062651.070320.081318.0NaNNaN5279549.0